Diagnostic accuracy of a set of clinical and radiological criteria for screening of COVID-19 using RT-PCR as the reference standard

Evaluation of the EasyNAT SARS-CoV-2 assay PCR test for the diagnosis of SARS-CoV-2 infection

Reverse transcription polymerase chain reaction (RT-PCR) tests are commonly utilized in commercial settings but pose challenges due to labor-intensive procedures and extended response times during peak demand. In contrast, real-time fluorescence and isothermal amplification assays using Crossing Priming Amplification (CPA) offer faster genetic material analysis, eliminate subjectivity, and require less manipulation and personnel training. This study aimed to validate the EasyNAT SARS-CoV-2 Assay, a diagnostic kit based on CPA, using oral and nasopharyngeal samples. The EasyNAT kit was compared to the Xpert Xpress SARS-CoV-2 kit, evaluating 873 samples obtained during routine analysis at the Microbiology Laboratory of the Hospital Costa del Sol (Marbella, Spain). The overall sensitivity and specificity for the EasyNAT SARS-CoV-2 Assay were 79.1% (95%CI 74.5-83.7) and 99.5% (95%CI 98.7-100), respectively; with, validity index of 91.9%, positive predictive value of 98.9%, negative predictive value of 88.9%, positive likelihood ratio of 144.5, negative likelihood ratio of 0.21 and a total Youden Index of 0.79. Notably, sensitivity improved in fresh samples (91.4%), along with a high Youden Index (0.91). The EasyNAT SARS-CoV-2 Assay achieved a higher percentage of concordance in positive samples with Xpert Xpress SARS-CoV-2 when analyzing cycle threshold (Ct) intervals below 30 compared to intervals equal or greater than 30, and demons. In conclusion, the EasyNAT SARS-CoV-2 Assay demonstrated high sensitivity and agreement with Xpert Xpress SARS-CoV-2, particularly in fresh samples or when the signal was detected at Ct intervals below 30, indicating higher viral loads. This makes it suitable for rapid screening in various settings, including those with limited access to conventional molecular laboratory setting.

Unraveling the Dynamics of SARS-CoV-2 Mutations: Insights from Surface Plasmon Resonance Biosensor Kinetics

Surface Plasmon Resonance (SPR) technology is known to be a powerful tool for studying biomolecular interactions because it offers real-time and label-free multiparameter analysis with high sensitivity. This article summarizes the results that have been obtained from the use of SPR technology in studying the dynamics of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) mutations. This paper will begin by introducing the working principle of SPR and the kinetic parameters of the sensorgram, which include the association rate constant (ka), dissociation rate constant (kd), equilibrium association constant (KA), and equilibrium dissociation constant (KD). At the end of the paper, we will summarize the kinetic data on the interaction between angiotensin-converting enzyme 2 (ACE2) and SARS-CoV-2 obtained from the results of SPR signal analysis. ACE2 is a material that mediates virus entry. Therefore, understanding the kinetic changes between ACE2 and SARS-CoV-2 caused by the mutation will provide beneficial information for drug discovery, vaccine development, and other therapeutic purposes.

Machine Learning for COVID-19 Determination Using Surface-Enhanced Raman Spectroscopy

The rapid, low cost, and efficient detection of SARS-CoV-2 virus infection, especially in clinical samples, remains a major challenge. A promising solution to this problem is the combination of a spectroscopic technique: surface-enhanced Raman spectroscopy (SERS) with advanced chemometrics based on machine learning (ML) algorithms. In the present study, we conducted SERS investigations of saliva and nasopharyngeal swabs taken from a cohort of patients (saliva: 175; nasopharyngeal swabs: 114). Obtained SERS spectra were analyzed using a range of classifiers in which random forest (RF) achieved the best results, e.g., for saliva, the precision and recall equals 94.0% and 88.9%, respectively. The results demonstrate that even with a relatively small number of clinical samples, the combination of SERS and shallow machine learning can be used to identify SARS-CoV-2 virus in clinical practice.

Accuracy of the persistent AKI risk index in predicting acute kidney injury in patients admitted to the intensive care unit for acute respiratory failure

OBJECTIVE

To evaluate the accuracy of the persistent AKI risk index (PARI) in predicting acute kidney injury within 72 hours after admission to the intensive care unit, persistent acute kidney injury, renal replacement therapy, and death within 7 days in patients hospitalized due to acute respiratory failure.

METHODS

This study was done in a cohort of diagnoses of consecutive adult patients admitted to the intensive care unit of eight hospitals in Curitiba, Brazil, between March and September 2020 due to acute respiratory failure secondary to suspected COVID-19. The COVID-19 diagnosis was confirmed or refuted by RT-PCR for the detection of SARS-CoV-2. The ability of PARI to predict acute kidney injury at 72 hours, persistent acute kidney injury, renal replacement therapy, and death within 7 days was analyzed by ROC curves in comparison to delta creatinine, SOFA, and APACHE II.

RESULTS

Of the 1,001 patients in the cohort, 538 were included in the analysis. The mean age was 62 ± 17 years, 54.8% were men, and the median APACHE II score was 12. At admission, the median SOFA score was 3, and 83.3% had no renal dysfunction. After admission to the intensive care unit, 17.1% had acute kidney injury within 72 hours, and through 7 days, 19.5% had persistent acute kidney injury, 5% underwent renal replacement therapy, and 17.1% died. The PARI had an area under the ROC curve of 0.75 (0.696 - 0.807) for the prediction of acute kidney injury at 72 hours, 0.71 (0.613 - 0.807) for renal replacement therapy, and 0.64 (0.565 - 0.710) for death.

CONCLUSION

The PARI has acceptable accuracy in predicting acute kidney injury within 72 hours and renal replacement therapy within 7 days of admission to the intensive care unit, but it is not significantly better than the other scores.

Diagnostic evaluation of Panbio™ antigen rapid diagnostic test for SARS-CoV-2: A systematic review and meta-analysis

INTRODUCTION

The reverse transcriptase polymerase chain reaction (RT-PCR) is the reference diagnostic method for the confirmation of SARS-CoV-2 infected cases. However, various antigen rapid diagnostic tests (Ag-RDTs) have been developed. The purpose of this meta-analysis study was to assess the diagnostic performance of Panbio™ Ag-RDT (Abbott Point of Care) in identifying the SARS-CoV-2 virus.

METHODS

We systematically searched eight databases from March 2020 until March 2023 to look for potentially eligible articles. Diagnostic meta-analysis of Panbio™ Ag-RDT used diverse evaluation indicators, including sensitivity, specificity, Diagnostic Odds Ratio (DOR), and the area under the curve (AUC) value.

RESULTS

Of the 794 articles identified, 49 studies met the inclusion criteria. The pooled estimates of Panbio™ Ag-RDT for the diagnosis of SARS-CoV-2 were 0,65 (95% CI: 0,64-0,66), 0,99 (95% CI: 0,99-1,00), 578,03 (95% CI: 333,37-1002,26) for sensitivity, specificity, and DOR, respectively. Moreover, the summary receiver operating characteristic (SROC) curve revealed an AUC value of 0,942 (95% CI: 0,941-0,943), suggesting an outstanding diagnostic accuracy. Subgroup and meta-regression analyses showed that continent, study period, age, study population and cycle threshold (Ct) values constituted a source of heterogeneity. Furthermore, we demonstrated proof of publication bias for DOR values analyzed using Deek's test (p = 0,001) and funnel plot.

CONCLUSION

Panbio™ Ag-RDT presented an outstanding diagnostic accuracy in the detection of the SARS-CoV-2 virus in both adults and children with or without symptoms.

Typical phenotypes of patients with acute respiratory failure with and without COVID-19 and their relationship with outcomes: a cohort study

OBJECTIVE

To compare, within a cohort of patients with acute respiratory failure, the phenotypes of patients with and without COVID-19 in the context of the pandemic and evaluate whether COVID-19 is an independent predictor of intensive care unit mortality.

METHODS

This historical cohort study evaluated 1001 acute respiratory failure patients with suspected COVID-19 admitted to the intensive care unit of 8 hospitals. Patients were classified as COVID-19 cases and non-COVID-19 cases according to real-time polymerase chain reaction results. Data on clinical and demographic characteristics were collected on intensive care unit admission, as well as daily clinical and laboratory data and intensive care unit outcomes.

RESULTS

Although the groups did not differ in terms of APACHE II or SOFA scores at admission, the COVID-19 group had more initial symptoms of fever, myalgia and diarrhea, had a longer duration of symptoms, and had a higher prevalence of obesity. They also had a lower PaO2/FiO2 ratio, lower platelet levels than non-COVID-19 patients, and more metabolic changes, such as higher levels of blood glucose, C-reactive protein, and lactic dehydrogenase. Patients with non-COVID-19 acute respiratory failure had a higher prevalence of chronic obstructive pulmonary disease/asthma and cardiopathy. Patients with COVID-19 stayed in the hospital longer and had more complications, such as acute kidney failure, severe acute respiratory distress syndrome and severe infection. The all-cause mortality rate was also higher in this group (43.7% in the COVID-19 group versus 27.4% in the non-COVID-19 group). The diagnosis of COVID-19 was a predictor of intensive care unit mortality (odds ratio, 2.77; 95%CI, 1.89 - 4.07; p < 0.001), regardless of age or Charlson Comorbidity Index score.

CONCLUSION

In a prospective cohort of patients admitted with acute respiratory failure, patients with COVID-19 had a clearly different phenotype and a higher mortality than non-COVID-19 patients. This may help to outline more accurate screening and appropriate and timely treatment for these patients.

Dysglycemias in patients admitted to ICUs with severe acute respiratory syndrome due to COVID-19 versus other causes - a cohort study

BACKGROUND

Dysglycemias have been associated with worse prognosis in critically ill patients with COVID-19, but data on the association of dysglycemia with COVID-19 in comparison with other forms of severe acute respiratory syndrome are lacking. This study aimed to compare the occurrence of different glycemic abnormalities in patients with severe acute respiratory syndrome and COVID-19 admitted to intensive care units versus glycemic abnormalities in patients with severe acute respiratory syndrome from other causes, to evaluate the adjusted attributable risk associated with COVID-19 and dysglycemia and to assess the influence of these dysglycemias on mortality.

METHODS

We conducted a retrospective cohort of consecutive patients with severe acute respiratory syndrome and suspected COVID-19 hospitalized in intensive care units between March 11 and September 13, 2020, across eight hospitals in Curitiba-Brazil. The primary outcome was the influence of COVID-19 on the variation of the following parameters of dysglycemia: highest glucose level at admission, mean and highest glucose levels during ICU stay, mean glucose variability, percentage of days with hyperglycemia, and hypoglycemia during ICU stay. The secondary outcome was the influence of COVID-19 and each of the six parameters of dysglycemia on hospital mortality within 30 days from ICU admission.

RESULTS

The sample consisted of 841 patients, of whom 703 with and 138 without COVID-19. Comparing patients with and without COVID-19, those with COVID-19 had significantly higher glucose peaks at admission (165 mg/dL vs. 146 mg/dL; p = 0.002) and during ICU stay (242 mg/dL vs. 187md/dL; p < 0.001); higher mean daily glucose (149.7 mg/dL vs. 132.6 mg/dL; p < 0.001); higher percentage of days with hyperglycemia during ICU stay (42.9% vs. 11.1%; p < 0.001); and greater mean glucose variability (28.1 mg/dL vs. 25.0 mg/dL; p = 0.013). However, these associations were no longer statistically significant after adjustment for Acute Physiology and Chronic Health Evaluation II scores, Sequential Organ Failure Assessment scores, and C-reactive protein level, corticosteroid use and nosocomial infection. Dysglycemia and COVID-19 were each independent risk factors for mortality. The occurrence of hypoglycemia (< 70 mg/dL) during ICU stay was not associated with COVID-19.

CONCLUSION

Patients with severe acute respiratory syndrome due to COVID-19 had higher mortality and more frequent dysglycemia than patients with severe acute respiratory syndrome due to other causes. However, this association did not seem to be directly related to the SARS-CoV-2 infection.